






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case     					=		logging.get_logger(__name__)


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   int						    =     MobileViTConfig()

  # size of the architecture
  if "mobilevit_s" in mobilevit_name:
    SCREAMING_SNAKE_CASE							:   List[Any]						    =     [144, 192, 240]
    SCREAMING_SNAKE_CASE							:   Tuple						    =     [16, 32, 64, 96, 128, 160, 640]
  elif "mobilevit_xs" in mobilevit_name:
    SCREAMING_SNAKE_CASE							:   List[str]						    =     [96, 120, 144]
    SCREAMING_SNAKE_CASE							:   Dict						    =     [16, 32, 48, 64, 80, 96, 384]
  elif "mobilevit_xxs" in mobilevit_name:
    SCREAMING_SNAKE_CASE							:   List[str]						    =     [64, 80, 96]
    SCREAMING_SNAKE_CASE							:   List[str]						    =     [16, 16, 24, 48, 64, 80, 320]
    SCREAMING_SNAKE_CASE							:   int						    =     0.05
    SCREAMING_SNAKE_CASE							:   int						    =     2.0

  if mobilevit_name.startswith("deeplabv3_"					):
    SCREAMING_SNAKE_CASE							:   str						    =     512
    SCREAMING_SNAKE_CASE							:   List[str]						    =     16
    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     21
    SCREAMING_SNAKE_CASE							:   Dict						    =     "pascal-voc-id2label.json"
  else:
    SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     1000
    SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     "imagenet-1k-id2label.json"

  SCREAMING_SNAKE_CASE							:   Any						    =     "huggingface/label-files"
  SCREAMING_SNAKE_CASE							:   Tuple						    =     json.load(open(hf_hub_download(lowercase    ,  lowercase    ,  repo_type="dataset"					)    ,  "r"					)					)
  SCREAMING_SNAKE_CASE							:   List[str]						    =     {int(lowercase					): v for k, v in idalabel.items()}
  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     idalabel
  SCREAMING_SNAKE_CASE							:   str						    =     {v: k for k, v in idalabel.items()}

  return config


def 	lowerCamelCase__      (  lowercase    ,  lowercase=False					):

  """simple docstring"""






  for i in range(1    ,  6					):
    if F'''layer_{i}.''' in name:
      SCREAMING_SNAKE_CASE							:   Tuple						    =     name.replace(F'''layer_{i}.'''    ,  F'''encoder.layer.{i - 1}.'''					)

  if "conv_1." in name:
    SCREAMING_SNAKE_CASE							:   Dict						    =     name.replace("conv_1."    ,  "conv_stem."					)
  if ".block." in name:
    SCREAMING_SNAKE_CASE							:   List[str]						    =     name.replace(".block."    ,  "."					)

  if "exp_1x1" in name:
    SCREAMING_SNAKE_CASE							:   str						    =     name.replace("exp_1x1"    ,  "expand_1x1"					)
  if "red_1x1" in name:
    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     name.replace("red_1x1"    ,  "reduce_1x1"					)
  if ".local_rep.conv_3x3." in name:
    SCREAMING_SNAKE_CASE							:   Dict						    =     name.replace(".local_rep.conv_3x3."    ,  ".conv_kxk."					)
  if ".local_rep.conv_1x1." in name:
    SCREAMING_SNAKE_CASE							:   int						    =     name.replace(".local_rep.conv_1x1."    ,  ".conv_1x1."					)
  if ".norm." in name:
    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     name.replace(".norm."    ,  ".normalization."					)
  if ".conv." in name:
    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     name.replace(".conv."    ,  ".convolution."					)
  if ".conv_proj." in name:
    SCREAMING_SNAKE_CASE							:   Dict						    =     name.replace(".conv_proj."    ,  ".conv_projection."					)

  for i in range(0    ,  2					):
    for j in range(0    ,  4					):
      if F'''.{i}.{j}.''' in name:
        SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     name.replace(F'''.{i}.{j}.'''    ,  F'''.{i}.layer.{j}.'''					)

  for i in range(2    ,  6					):
    for j in range(0    ,  4					):
      if F'''.{i}.{j}.''' in name:
        SCREAMING_SNAKE_CASE							:   Optional[int]						    =     name.replace(F'''.{i}.{j}.'''    ,  F'''.{i}.'''					)
        if "expand_1x1" in name:
          SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     name.replace("expand_1x1"    ,  "downsampling_layer.expand_1x1"					)
        if "conv_3x3" in name:
          SCREAMING_SNAKE_CASE							:   str						    =     name.replace("conv_3x3"    ,  "downsampling_layer.conv_3x3"					)
        if "reduce_1x1" in name:
          SCREAMING_SNAKE_CASE							:   List[Any]						    =     name.replace("reduce_1x1"    ,  "downsampling_layer.reduce_1x1"					)

  for i in range(2    ,  5					):
    if F'''.global_rep.{i}.weight''' in name:
      SCREAMING_SNAKE_CASE							:   Optional[int]						    =     name.replace(F'''.global_rep.{i}.weight'''    ,  ".layernorm.weight"					)
    if F'''.global_rep.{i}.bias''' in name:
      SCREAMING_SNAKE_CASE							:   str						    =     name.replace(F'''.global_rep.{i}.bias'''    ,  ".layernorm.bias"					)

  if ".global_rep." in name:
    SCREAMING_SNAKE_CASE							:   Dict						    =     name.replace(".global_rep."    ,  ".transformer."					)
  if ".pre_norm_mha.0." in name:
    SCREAMING_SNAKE_CASE							:   Tuple						    =     name.replace(".pre_norm_mha.0."    ,  ".layernorm_before."					)
  if ".pre_norm_mha.1.out_proj." in name:
    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     name.replace(".pre_norm_mha.1.out_proj."    ,  ".attention.output.dense."					)
  if ".pre_norm_ffn.0." in name:
    SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     name.replace(".pre_norm_ffn.0."    ,  ".layernorm_after."					)
  if ".pre_norm_ffn.1." in name:
    SCREAMING_SNAKE_CASE							:   Tuple						    =     name.replace(".pre_norm_ffn.1."    ,  ".intermediate.dense."					)
  if ".pre_norm_ffn.4." in name:
    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     name.replace(".pre_norm_ffn.4."    ,  ".output.dense."					)
  if ".transformer." in name:
    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     name.replace(".transformer."    ,  ".transformer.layer."					)

  if ".aspp_layer." in name:
    SCREAMING_SNAKE_CASE							:   int						    =     name.replace(".aspp_layer."    ,  "."					)
  if ".aspp_pool." in name:
    SCREAMING_SNAKE_CASE							:   Tuple						    =     name.replace(".aspp_pool."    ,  "."					)
  if "seg_head." in name:
    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     name.replace("seg_head."    ,  "segmentation_head."					)
  if "segmentation_head.classifier.classifier." in name:
    SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     name.replace("segmentation_head.classifier.classifier."    ,  "segmentation_head.classifier."					)

  if "classifier.fc." in name:
    SCREAMING_SNAKE_CASE							:   List[Any]						    =     name.replace("classifier.fc."    ,  "classifier."					)
  elif (not base_model) and ("segmentation_head." not in name):
    SCREAMING_SNAKE_CASE							:   List[Any]						    =     "mobilevit." + name

  return name


def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase=False					):

  """simple docstring"""






  if base_model:
    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     ""
  else:
    SCREAMING_SNAKE_CASE							:   Any						    =     "mobilevit."

  for key in orig_state_dict.copy().keys():
    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     orig_state_dict.pop(lowercase					)

    if key[:8] == "encoder.":
      SCREAMING_SNAKE_CASE							:   int						    =     key[8:]

    if "qkv" in key:
      SCREAMING_SNAKE_CASE							:   Optional[int]						    =     key.split("."					)
      SCREAMING_SNAKE_CASE							:   Any						    =     int(key_split[0][6:]					) - 1
      SCREAMING_SNAKE_CASE							:   List[Any]						    =     int(key_split[3]					)
      SCREAMING_SNAKE_CASE							:   List[Any]						    =     model.get_submodule(F'''{model_prefix}encoder.layer.{layer_num}'''					)
      SCREAMING_SNAKE_CASE							:   int						    =     layer.transformer.layer[transformer_num].attention.attention.all_head_size
      SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     (
          F'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
      )
      if "weight" in key:
        SCREAMING_SNAKE_CASE							:   Optional[int]						    =     val[:dim, :]
        SCREAMING_SNAKE_CASE							:   Tuple						    =     val[dim : dim * 2, :]
        SCREAMING_SNAKE_CASE							:   Dict						    =     val[-dim:, :]
      else:
        SCREAMING_SNAKE_CASE							:   str						    =     val[:dim]
        SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     val[dim : dim * 2]
        SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     val[-dim:]
    else:
      SCREAMING_SNAKE_CASE							:   List[Any]						    =     val

  return orig_state_dict


def 	lowerCamelCase__      (  ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   List[str]						    =     "http://images.cocodataset.org/val2017/000000039769.jpg"
  SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     Image.open(requests.get(lowercase    ,  stream=lowercase					).raw					)
  return im


@torch.no_grad()
def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase    ,  lowercase=False					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Any						    =     get_mobilevit_config(lowercase					)

  # load original state_dict
  SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     torch.load(lowercase    ,  map_location="cpu"					)

  # load 🤗 model
  if mobilevit_name.startswith("deeplabv3_"					):
    SCREAMING_SNAKE_CASE							:   List[str]						    =     MobileViTForSemanticSegmentation(lowercase					).eval()
  else:
    SCREAMING_SNAKE_CASE							:   str						    =     MobileViTForImageClassification(lowercase					).eval()

  SCREAMING_SNAKE_CASE							:   Any						    =     convert_state_dict(lowercase    ,  lowercase					)
  model.load_state_dict(lowercase					)

  # Check outputs on an image, prepared by MobileViTImageProcessor
  SCREAMING_SNAKE_CASE							:   List[Any]						    =     MobileViTImageProcessor(crop_size=config.image_size    ,  size=config.image_size + 32					)
  SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     image_processor(images=prepare_img()    ,  return_tensors="pt"					)
  SCREAMING_SNAKE_CASE							:   List[Any]						    =     model(**lowercase					)
  SCREAMING_SNAKE_CASE							:   str						    =     outputs.logits

  if mobilevit_name.startswith("deeplabv3_"					):
    assert logits.shape == (1, 21, 32, 32)

    if mobilevit_name == "deeplabv3_mobilevit_s":
      SCREAMING_SNAKE_CASE							:   Optional[int]						    =     torch.tensor(
          [
              [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
              [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
              [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
          ]					)
    elif mobilevit_name == "deeplabv3_mobilevit_xs":
      SCREAMING_SNAKE_CASE							:   Tuple						    =     torch.tensor(
          [
              [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
              [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
              [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
          ]					)
    elif mobilevit_name == "deeplabv3_mobilevit_xxs":
      SCREAMING_SNAKE_CASE							:   List[Any]						    =     torch.tensor(
          [
              [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
              [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
              [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
          ]					)
    else:
      raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''					)

    assert torch.allclose(logits[0, :3, :3, :3]    ,  lowercase    ,  atol=1E-4					)
  else:
    assert logits.shape == (1, 1000)

    if mobilevit_name == "mobilevit_s":
      SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     torch.tensor([-0.9866, 0.2392, -1.1241]					)
    elif mobilevit_name == "mobilevit_xs":
      SCREAMING_SNAKE_CASE							:   Dict						    =     torch.tensor([-2.4761, -0.9399, -1.9587]					)
    elif mobilevit_name == "mobilevit_xxs":
      SCREAMING_SNAKE_CASE							:   Tuple						    =     torch.tensor([-1.9364, -1.2327, -0.4653]					)
    else:
      raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''					)

    assert torch.allclose(logits[0, :3]    ,  lowercase    ,  atol=1E-4					)

  Path(lowercase					).mkdir(exist_ok=lowercase					)
  print(F'''Saving model {mobilevit_name} to {pytorch_dump_folder_path}'''					)
  model.save_pretrained(lowercase					)
  print(F'''Saving image processor to {pytorch_dump_folder_path}'''					)
  image_processor.save_pretrained(lowercase					)

  if push_to_hub:
    SCREAMING_SNAKE_CASE							:   List[str]						    =     {
        "mobilevit_s": "mobilevit-small",
        "mobilevit_xs": "mobilevit-x-small",
        "mobilevit_xxs": "mobilevit-xx-small",
        "deeplabv3_mobilevit_s": "deeplabv3-mobilevit-small",
        "deeplabv3_mobilevit_xs": "deeplabv3-mobilevit-x-small",
        "deeplabv3_mobilevit_xxs": "deeplabv3-mobilevit-xx-small",
    }

    print("Pushing to the hub..."					)
    SCREAMING_SNAKE_CASE							:   int						    =     model_mapping[mobilevit_name]
    image_processor.push_to_hub(lowercase    ,  organization="apple"					)
    model.push_to_hub(lowercase    ,  organization="apple"					)


if __name__ == "__main__":
   snake_case     					=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--mobilevit_name""",
       default="""mobilevit_s""",
       type=str,
       help=(
           """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
           """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
       ),
   )
   parser.add_argument(
       """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
   )
   parser.add_argument(
       """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
   )

   snake_case     					=		parser.parse_args()
   convert_movilevit_checkpoint(
       args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )







'''simple docstring'''




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase					=logging.get_logger(__name__)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''projector.weight''']
    __lowerCamelCase            =    downstream_dict['''projector.bias''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''model.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       List[Any]:
    __lowerCamelCase            =    UniSpeechSatForXVector.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''connector.weight''']
    __lowerCamelCase            =    downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
        __lowerCamelCase            =    downstream_dict[
            f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
        ]
        __lowerCamelCase            =    downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    __lowerCamelCase            =    downstream_dict['''objective.W''']
    return model






@torch.no_grad()
def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location='''cpu'''							)

    __lowerCamelCase            =    checkpoint['''Downstream''']

    __lowerCamelCase            =    UniSpeechSatConfig.from_pretrained(UpperCamelCase__							)
    __lowerCamelCase            =    WavaVecaFeatureExtractor.from_pretrained(
        UpperCamelCase__							,    return_attention_mask=UpperCamelCase__							,    do_normalize=UpperCamelCase__							)

    __lowerCamelCase            =    hf_config.architectures[0]
    if arch.endswith('''ForSequenceClassification'''							):
        __lowerCamelCase            =    convert_classification(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForAudioFrameClassification'''							):
        __lowerCamelCase            =    convert_diarization(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForXVector'''							):
        __lowerCamelCase            =    convert_xvector(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    else:
        raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""							)

    if hf_config.use_weighted_layer_sum:
        __lowerCamelCase            =    checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(UpperCamelCase__							)
    hf_model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  parser.add_argument(
      "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  )
  parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  __UpperCAmelCase					=parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
